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Knowledge-Based Energy Functions for Computational Studies of Proteins

  • Chapter
Computational Methods for Protein Structure Prediction and Modeling

Part of the book series: BIOLOGICAL AND MEDICAL PHYSICS BIOMEDICAL ENGINEERING ((BIOMEDICAL))

Abstract

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design.We discuss in some detail the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and nonlinear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.

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  1. Xiang Li
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  2. Jie Liang
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Editors and Affiliations

  1. Department of Biochemistry and Molecular Biology, University of Georgia, 120 Green Street, 30602, Athens, GA, USA

    Ying Xu

  2. Department of Computer Science Digital Biology Laboratory, University of Missouri-Columbia, 201 Engineering Building West Columbia, 65211, Columbia, MO, USA

    Dong Xu

  3. Department of Bioengineering Center for Bioinformatics, University of Illinois at Chicago, 851 S. Morgan Street, 60607, Chicago, IL, USA

    Jie Liang

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© 2007 Springer

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Li, X., Liang, J. (2007). Knowledge-Based Energy Functions for Computational Studies of Proteins. In: Xu, Y., Xu, D., Liang, J. (eds) Computational Methods for Protein Structure Prediction and Modeling. BIOLOGICAL AND MEDICAL PHYSICS BIOMEDICAL ENGINEERING. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68372-0_3

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